I’ll get back to rpncalc shortly, but before I do, I wanted to take a post to talk about a surprising problem I recently had with lazy sequences. As part of my day job, I am developing a Clojure based system for accumulating and displaying time series data on a web page. One of the core algorithms in my
implementation is a incremental merge sort. I have a function that takes two seq’s, both ordered by time, and produces a lazy result seq with all values from both inputs, also in time order. Every few seconds, as new input values are read from their sources, the program uses the ordered merge function to integrate the new values into a seq that contains a complete history of all values. It’s a straightforward and flexible design, and initially, it appeared to work quite well. The problems only started to arise after several hours of run time: traversing the history list would then immediately result in stack overflow exceptions.

If you’re familiar with lazy sequences, this may seem like an odd result. After all, one of the benefits of lazy sequences (aside from their laziness) is that they can eliminate recursion and reduce pressure on the stack. Lazy sequences might require more heap allocation, but they shouldn’t require all that much stack. To explore this idea a bit further, I’m going to use a simpler example than my merge function, I’m going to start with a recursive version of map:

What’s happening here is that the recursive call to my-map causes the function to allocate a stack frame for each element of the input sequence. The stack has a fixed size limit, so this places a fixed limit on the size of the sequence that my-map can manipulate. Any input sequences beyond that length limit will cause the function to overflow the stack. map gets around this through laziness, which is something that we can also use:

While the text of my-map and my-map-lazy is similar, the functions internally are quite different in
operation. my-map completely computes the result of the mapping before it returns: it eagerly evaluates and returns the fully calculated result. In contrast, my-map-lazy doesn’t compute any of the mapping before it returns: it lazily defers the calculation until later and returns a promise to compute the result later on. The difference may be more clear, looking at a slightly macro-expanded form of my-map-lazy:

The only computations that happen between the entry and exit of my-map-lazy are the allocation of a new lexical closure and then the instantiation of a new instance of LazySeq. While the body my-map-lazy still contains a call to itself, the call doesn’t happen until after my-map-lazy returns and the LazySeq invokes the closure. There is no recursive call, and there is no risk of overflowing the stack. (The traversal state that was stored on the stack in the recursive version is stored on the heap in the lazy version.)

So why was my merge sort overflowing the stack? To see why, I’m going to introduce a new function, using Clojure’s internal map function. This function serves no purpose, other than to introduce a layer of laziness. It is only useful for the purposes of this discussion:

(defn lazify [ xs ]
(map identity xs))

Because the evaluation of map is lazy, we can predict that what lazify returns is a LazySeq. This turns out to be true:

Due to the way lazify is defined, the results of the sequences a and b are identical to each other; They both result in (1 2 3 4 5). However, despite the similarity in the results they produce, the two sequences are distinct and produce their results with different code paths. Sequence a computes the identity of each element of the vector [1 2 3 4 5] and sequence b computes the identity of each element of sequence a. Sequence b has to go through sequence a to get the value from the vector that underlies both. Even in lazy sequences, this process is still eager, still recursive, and it still consumes stack.

To confirm this theory, I’ll use another function that applies lazify to a sequence any number of times.

This function builds a tower of lazy sequences n sequences tall. Computing even the first element of the result sequence, involves recursively computing every element of each sequence in this tower, down to the original input seq to lazify-n. The depth of the stack required to maintain this recursive stack is proprortional to n. High values of n should produce sequences that can’t be traversed without throwing a stack overflow error. This turns out to be true:

Going back to my original merge sort stack overflow, it is caused by the same issue that we see in lazify-n. The calls to merge two lists don’t merge the lists at the time of the call. Rather, the calls produce promises to merge the lists at some later point in time. Every call to merge increases the number of lists to merge, and increases the depth of the stack that the merge process needs to use during the merge operation. After a while, the number of lists to be merged gets high enough that they can’t be merged without overflowing the stack. This is the cause of my initial stack overflow.

So what’s the solution? One easy solution is to give up some amount of laziness.

The only difference between this new version of lazify-n and the previous is the call to doall on the fourth line. What doall does is force the full evaluation of a lazy sequence. So, while lazify-n! still produces an n high tower of lazy sequences, they’re all been fully traversed. Because LazySeq caches values the first time it’s traversed traversal, there’s no need to recursively call up the tower of sequences to traverse the final output sequence. This gives up some laziness, but it avoids both stack overflow issues we’ve discussed in this blog post: the overflow on long input sequence lengths and the overflow on deeply nested lazy sequences. The cost (there’s always a cost) is that this requires more heap storage than many alternative structures.

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